Add to ORKGWe explored the usage of Monte Carlo tree search (MCTS) and deep learning in order to predict game level difficulty in Candy Crush Saga (Candy) measured as number of attempts per success. A deep neural network (DNN) was trained to predict moves from game states from large amounts of game play data. The DNN played a diverse set of levels in Candy and a regression model was fitted to predict human difficulty from bot difficulty. We compared our results to an MCTS bot. Our results show that the DNN can make estimations of game level difficulty comparable to MCTS in substantially shorter time. Vi utforskade användning av Monte Carlo tree search (MCTS) och deep learning för attuppskatta banors svårighetsgrad i Candy Crush Saga (Candy). Ett deep ...
In this thesis we present two approaches to improve automatic playtesting using player modeling. By ...
In this thesis we present two approaches to improve automatic playtesting using player modeling. By ...
For more than the last decade, Monte Carlo Tree Search (MCTS) has been the basis of most of the winn...
We explored the usage of Monte Carlo tree search (MCTS) and deep learning in order to predict game l...
This thesis presents an approach to predict the difficulty of levels in a game by simulating game pl...
This thesis presents an approach to predict the difficulty of levels in a game by simulating game pl...
This thesis presents an approach to predict the difficulty of levels in a game by simulating game pl...
The purpose of this thesis is to evaluate the possibility of predicting difficulty, measured in aver...
This paper proposes using a linear function approximator, rather than a deep neural network (DNN), t...
This paper proposes using a linear function approximator, rather than a deep neural network (DNN), t...
This paper proposes using a linear function approximator, rather than a deep neural network (DNN), t...
This thesis aims to investigate general game-playing by conducting a comparison between the well-kno...
This degree project presents a reinforcement learning (RL) approach called deep Q-network (DQN) for ...
This degree project presents a reinforcement learning (RL) approach called deep Q-network (DQN) for ...
This degree project presents a reinforcement learning (RL) approach called deep Q-network (DQN) for ...
In this thesis we present two approaches to improve automatic playtesting using player modeling. By ...
In this thesis we present two approaches to improve automatic playtesting using player modeling. By ...
For more than the last decade, Monte Carlo Tree Search (MCTS) has been the basis of most of the winn...
We explored the usage of Monte Carlo tree search (MCTS) and deep learning in order to predict game l...
This thesis presents an approach to predict the difficulty of levels in a game by simulating game pl...
This thesis presents an approach to predict the difficulty of levels in a game by simulating game pl...
This thesis presents an approach to predict the difficulty of levels in a game by simulating game pl...
The purpose of this thesis is to evaluate the possibility of predicting difficulty, measured in aver...
This paper proposes using a linear function approximator, rather than a deep neural network (DNN), t...
This paper proposes using a linear function approximator, rather than a deep neural network (DNN), t...
This paper proposes using a linear function approximator, rather than a deep neural network (DNN), t...
This thesis aims to investigate general game-playing by conducting a comparison between the well-kno...
This degree project presents a reinforcement learning (RL) approach called deep Q-network (DQN) for ...
This degree project presents a reinforcement learning (RL) approach called deep Q-network (DQN) for ...
This degree project presents a reinforcement learning (RL) approach called deep Q-network (DQN) for ...
In this thesis we present two approaches to improve automatic playtesting using player modeling. By ...
In this thesis we present two approaches to improve automatic playtesting using player modeling. By ...
For more than the last decade, Monte Carlo Tree Search (MCTS) has been the basis of most of the winn...